Curve-Fit Downloads & RevenueCurve-Fit · App Store
Curve-Fit is #177 in Productivity Paid in China and charting in 1 of 30 countries we track.
Updated 8 Nov 2026 · rankings refresh through the day
DeveloperBjarne Berge
CategoryProductivity
Price8.00 CNY
Released
Rating★ 5.0 (1)
Version7.0
Age rating12+
First seen by us
29 Sep 2026
29 Sep 2026Installs (est.)100+ worldwide
Full timeline · 3 events
- 🆕Curve-Fit first spotted by AppInsider#49 in Top Paid · All Apps · Spain
- 📦Curve-Fit version 7.0Updated with Xcode 26.
- 🆕Curve-Fit released on the App Store
Collected from the stores by AppInsider since we first spotted the app. Dates are when we saw each change.
Rating
5.0 ★
1 ratings
Measured
Best rank now
#177
Productivity PaidMeasured
Curve-Fit on the App Store
App details
App ID
1450077815
Publisher
Bjarne Berge
On the store since
Feb 2019
7 years on the store
Minimum OS
iOS 15.6+
iPhone 6s and later
Devices
iPhone · iPad
Universal purchase
Content rating
12+
Languages
1
EN
Size
1 MB
Also listed in
Utilities
Curve-Fit downloads
Worldwide, per day, and installs to date. The band is open; the exact number is free with an account.
How this is calculated
Apple publishes no download counts. App Store downloads come from chart positions and daily rating growth per country, calibrated on Google Play install counts; lifetime installs from ratings worldwide × installs per rating. Confidence is the chance the real number is within half to double ours.
Active users
All platforms, worldwide. Free with an account.
Stickiness (DAU / MAU)
how many monthly users open it on a given day
How this is calculated
Monthly users come from lifetime installs across every store, fitted on monthly active user figures companies publish; daily users = monthly × the typical daily/monthly ratio for the category. Confidence is the chance the real number is within half to double ours.
Where it ranks now
Every top chart it appears in across 30 countries.
| Country | Charts | Rank |
|---|---|---|
| China | ProductivityPaid | #177 |
Apps near Curve-Fit on the chart
Productivity Paid, around #177.
Ratings
App Store, worldwide.
Rating
5.0 ★
1 ratings
Measured
Ratings
1
Measured
About Curve-Fit
'CurveFit' uses regression analysis by the method of least squares to find best fit for a set of data to a selected equation.
The curve-fitting technique used in this app is based on regression analysis by the method of least squares. The free version fits a straight line through a data-set using least squares analysis.
One In-App purchase is required to fit the other equations to the data set:
Straight Line : Y = C0 + C1*X (free)
Power Curve : Y = C0 + X^C1 Exponential I : Y = C0 * EXP(C1*X)
Exponential II : Y = C0 * X * EXP(C1*X)
Hyperbolic : Y = (C0 + C1*X)/(1 - C2*X)
Square Root : Y = C0 + C1*SQRT(X)
Polynomial : Y = C0 + C1*X + --- + CN*X^N
Exponential Poly : Y = C0 * EXP(C1*X + --- +
Natural Log : Y = C0 + C1*(LN(X)) + --- +
Reciprocal : Y = C0 + C1/X + --- + CN/X^N
Most literature deals with least squares analysis for straight lines, 2nd degree polynomials, and functions that can be linearized. The input-data is transformed into a format that the can be put into linear forms with undetermined constants. These types of equations are applicable for least-squares regression.
The regression routine is needed for determining values for the set of unknown quantities C1, C2,- - - ,Cm in the equation:
Y = C1 x F1(X) + C2 x F2(X) + - - - + Cm x Fm(X)
The constants are determined to minimize the sum of squares of the differences between the measured values (Y1, Y2, - - - , Yn) and the predicted equation Yc = F(X) which is found by curve-fitting the given data.
The principle of least squares is to find the values for the unknowns C1 through Cm that will minimize the sum of the squares of the residuals:
n
∑(ri) = r12 + r22 + - - - + rn2 = minimum
i=1
This is done by letting the derivative of the above equation equal zero. Thereby there will be generated as many algebraic equations as given data points, and the number of equations will be larger than unknowns.
The curve-fitting technique used in this app is based on regression analysis by the method of least squares. The free version fits a straight line through a data-set using least squares analysis.
One In-App purchase is required to fit the other equations to the data set:
Straight Line : Y = C0 + C1*X (free)
Power Curve : Y = C0 + X^C1 Exponential I : Y = C0 * EXP(C1*X)
Exponential II : Y = C0 * X * EXP(C1*X)
Hyperbolic : Y = (C0 + C1*X)/(1 - C2*X)
Square Root : Y = C0 + C1*SQRT(X)
Polynomial : Y = C0 + C1*X + --- + CN*X^N
Exponential Poly : Y = C0 * EXP(C1*X + --- +
Natural Log : Y = C0 + C1*(LN(X)) + --- +
Reciprocal : Y = C0 + C1/X + --- + CN/X^N
Most literature deals with least squares analysis for straight lines, 2nd degree polynomials, and functions that can be linearized. The input-data is transformed into a format that the can be put into linear forms with undetermined constants. These types of equations are applicable for least-squares regression.
The regression routine is needed for determining values for the set of unknown quantities C1, C2,- - - ,Cm in the equation:
Y = C1 x F1(X) + C2 x F2(X) + - - - + Cm x Fm(X)
The constants are determined to minimize the sum of squares of the differences between the measured values (Y1, Y2, - - - , Yn) and the predicted equation Yc = F(X) which is found by curve-fitting the given data.
The principle of least squares is to find the values for the unknowns C1 through Cm that will minimize the sum of the squares of the residuals:
n
∑(ri) = r12 + r22 + - - - + rn2 = minimum
i=1
This is done by letting the derivative of the above equation equal zero. Thereby there will be generated as many algebraic equations as given data points, and the number of equations will be larger than unknowns.
Latest updates
7.0
Updated with Xcode 26.
